The goal of this research is to develop improved techniques for fully automated and computer-assisted classification of medical text. A primary focus will be mechanisms that support easy-to-construct classifiers, thus enabling research on existing collections of free-text that are now difficult to analyze. The proposed approach is exemplar-based: i.e., compare new text with a training set of previously classified texts, and use the classifications of the closest retrieved texts to generate suggested codes for the new text. Natural language extraction techniques will preprocess texts to assist the retrieval machinery. Phase I will conduct a series of experiments to test the approach, utilizing coded radiology and pathology reports from Brigham and Women's Hospital and HCHP in Boston. Phase II will develop a full software prototype and deploy it for on-site evaluation. Advanced retrieval techniques and expert system back ends for alarming will also be explored in Phase II. The major technical innovation is a novel combination of document retrieval and natural language extraction technologies to permit easy construction of automated medical text classifiers. The major health- related contribution is an enhanced ability to classify existing free-text records to permit statistical analysis for research and clinical quality- measurement initiatives.